Python Data Regression

Regression is used to train a model to predict a relationship between a dependent variable and one or more independent variables. Regression models can be linear or nonlinear, depending on the relationship between the dependent and independent variables. See the Machine Learning for Engineers course for additional information and the following tutorials on Linear Regression, Nonlinear Regression, Gaussian Processes, k-Nearest Neighbors, Neural Network Regressor, Support Vector Regressor, XGBoost Regressor: https://apmonitor.com/pds This tutorial is with linear regression to demonstrate a simple example in Python Gekko.

Example Multiple Linear Regression

Multiple linear regression models the relationship between a dependent variable and one or more independent variables. It is used when there are multiple independent variables that contribute to the prediction of the dependent variable. The goal of multiple linear regression is to find the best fit that minimizes the differences between the observed and predicted values of the dependent variable. Objective: Perform multiple linear regression on sample data with two inputs. Find unknown parameters c0-c2 to minimize the difference between measured ym and predicted yp subject to a constraint on the summation of c1 x1.

See https://apmonitor.com/do/index.php/Main/MultipleRegression for source code.